In contrast, one-stage object detectors have a fully convolutional architecture. The latter behaves as a single regression model predicting bounding boxes and class probabilities from the entire image, which provides training and inference speed, and the capability to generalize through different scenarios. Redmon et al. were the first to introduce this architecture in YOLOv1 [1]}. Although YOLOv1 achieved only 63% mAP on PASCAL VOC 2007 dataset, its inference speed was significantly higher than that of two-stage object detectors, while the recently proposed YOLOv4 in [2]} achieved a higher mAP with respect to the fully convolutional architecture.
